Development of machine-learning-based models for identifying the sources of nitrate and fluoride in groundwater and predicting their human health risks

Abstract

AbstractThis research aimed to identify the main sources of groundwater pollution and assess the non-carcinogenic human health risk resulting from nitrate and fluoride contamination. These goals were achieved by employing unsupervised and supervised machine algorithms, including principal component analysis (PCA) and multilayer perceptron artificial neural networks (MLP-ANN). Thirty-seven groundwater samples were analyzed for twelve physical and chemical parameters, including pH, EC, TDS, TH, Cl, F, SO4, NO3, Ca, Mg, Na, and HCO3, and the initial investigation indicated that except for Cl, F, Ca, and Mg, all the parameters are above the guidelines of the World Health Organization (WHO). PCA indicated that mineral dissolution is the main source of F, while high NO3 concentration primarily resulted from agricultural operation due to extensive use of nitrogen and calcium-based fertilizers. Consequently, the non-carcinogenic human health risk (HHR) for children and adults is evaluated based on NO3 and F. The conventional approach for assessing HHR is time-consuming and often associated with errors in calculating hazard quotients (HQ) and hazard indices (HI). In this research, MLP-ANN is suggested to overcome these limitations. In the MLP-ANN modeling, the data were divided into two parts training (80%) and validation (20%), with NO3 and F concentration as inputs and HQ and HI as outputs. The performance of the resulting models was tested using root mean square error (RMSE) and coefficient of determination (R2). The model provided a satisfactory result with a maximum RMSE of 4% and R2 higher than 97% for training and validation. As a result, obtained HIs suggested that 97.3% of the groundwater samples in the study area are suitable for human consumption. The non-carcinogenic HHR is successfully assessed using machine learning algorithms, and the results have led to the conclusion that this approach is highly recommended for effectively managing groundwater resources.